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Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2016. Sparse representation over learned dictionary for symbol recognition. SP, 125, 36–47.
Abstract: In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Keywords: Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
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Anton Cervantes, Gemma Sanchez, Josep Llados, Agnes Borras and A. Rodriguez. 2005. Biometric Recognition Based on Line Shape Descriptors. Sixth IAPR International Workshop on Graphics Recognition (GREC 2005).335–344.
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Joan Mas, Gemma Sanchez and Josep Llados. 2005. An Incremental Parser to Recognize Diagram Symbols and Gestures represented by Adjacency Grammars.
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N. Zakaria, Jean-Marc Ogier and Josep Llados. 2005. On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix.
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Josep Llados, Felipe Lumbreras, V. Chapaprieta and J. Queralt. 2001. ICAR: Identity Card Automatic Reader..
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Gemma Sanchez and Josep Llados. 2001. A Graph Grammar to Recognize Textured Symbols..
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Muhammad Muzzamil Luqman, Jean-Yves Ramel and Josep Llados. 2012. Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 243–253. (LNCS.)
Abstract: Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
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Volkmar Frinken, Alicia Fornes, Josep Llados and Jean-Marc Ogier. 2012. Bidirectional Language Model for Handwriting Recognition. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 611–619. (LNCS.)
Abstract: In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.
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Klaus Broelemann, Anjan Dutta, Xiaoyi Jiang and Josep Llados. 2012. Hierarchical graph representation for symbol spotting in graphical document images. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 529–538. (LNCS.)
Abstract: Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset.
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Jaume Gibert, Ernest Valveny, Horst Bunke and Alicia Fornes. 2012. On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer-Berlag, Berlin, 135–143. (LNCS.)
Abstract: Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.
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